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language:
- ko
- en
license: mit
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# Model Card for free-evo-qwen72b-v0.8

## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team

## 1st place : 2024 4th May - avg. 81.28 [Open Llm Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)   
but this kicked away. maybe the explanation was not enough.

## Method
- We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)

## Process
You need two models with the same architecture.
- Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower.
- Merge the two models.
- Evaluate the merged model.
- Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.)
- Merge the models again.
- Evaluate again.
- Keep going until the average evaluation score is higher than the original one.   

That's it. Simple.
You can create a framework to automate this process.

## Base Architecture 
- QWEN2

## Base Models
- several QWEN2 based models